The goal of my research is to build a theory of data-driven optimization, at the intersection of algorithms and machine learning. I am interested in developing novel optimization frameworks that are motivated by applications in machine learning. Recent shifts in optimization, such as an increasing dependence on data and new large scale applications, necessitate new models that capture modern challenges of decision-making. My research aims to establish foundations for such models through novel algorithmic machinery with theoretical guarantees, hardness results, and experimental evaluations that demonstrate the significance of these new frameworks and techniques.
I received my PhD in Computer Science from Harvard University where I was advised by Yaron Singer. My thesis was awarded an ACM SIGecom Doctoral Dissertation Honorable Mention. As a graduate student, I also received a Google PhD fellowship and a Smith Family Graduate Science and Engineering Fellowship. I also co-founded Robust Intelligence, an AI security startup.
Email: eb3224 [at] columbia.edu